Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

A Study of Optimization and Rule/Goal Graph for a Logical Query

Static optimization of logical queries is, in substance, to move selections down as far as possible in evaluating logical queries. This paper extends Ullman's RGG (Rule/Goal Graph) and introduces P- graph, with which a wide range of recursive logical queries can be statically optimized top-down and evaluated bottom-up, some of which are usually optimized by dynamic approaches. The paper also shows that for some logical queries the complexity of pushing selections down and computing bottom-up is related to the complexity of base relation in the queries.

Analysis of Indirect Evidence in Hit-and-run Cases

For personnel handling cases in public security bodies,evaluation of evidence is key to discover the fact and then to settle disputes.The process of evaluation shall be confirmed to rules of logic.Rules of logic are a set of judgment rules for people to reflect,induce,and deduce the development of law worldwide.Thus,evaluation of evidence shall be built on objective and rational logic deduction.Rules of logic applied in the investigation and judgment of evidence can help in finding a direct or indirect link between the evidence and the facts of an accident,thereby providing support and assistance for accident identification.It is particularly necessary to apply rules of logic to evaluate the indirect evidence chain in the investigation of hit‑and‑run traffic accident cases.This article demonstrates the specific application of such rules in the investigation of a serious hit‑and‑run traffic accident case.In the investigation process through the comprehensive analysis of and logical thinking around all indirect evidence,such as site inspection data,video monitoring data,witness testimony,and expert conclusions,and the combining of this with the direct evidence of the parties’statements,a complete chain of evidence for identifying a suspect’s vehicle and the accident facts can be formed.The effective application of rules of logic in the evaluation of the indirect evidence chain in hit‑and‑run traffic accident cases provides more clues and narrows the scope of an investigation,improving its efficiency and accuracy,thereby helping to identify the facts of an accident.